Artificial neural networks can be used to examine images and make predictions of the objects depicted in the images. These neural networks are computerized systems that are trained to identify objects in images. The training of the neural networks can include providing training images to the neural networks. The training images can be images with pixels that are labeled, or annotated, to reflect what type of object (e.g., object class) that each pixel represents. For example, each pixel in a training image can be associated with data or a datum indicative of what object the pixel depicts at least part of.
Creation of training images can be a time-intensive and costly endeavor. Some training images are created by one or more persons manually examining each pixel in an image and annotating or labeling the pixel with data or a datum to identify what object class is represented by the pixel. Some training images are created using crowd sourcing where several people who are not necessarily co-located can review and annotate images to speed up the process of creating training images. But, not all images can be annotated using crowd sourcing. Some images cannot be widely disseminated in a manner that allows for such crowd sourcing. For example, some images of damage to equipment used in connection with or subject to confidentiality agreements or restrictions, such as airplane engines, may not be able to be distributed amongst many people for crowd sourcing of the pixel annotation.